1 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
Junjie Ye, David Paz, Hengyuan Zhang +5
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent ap…
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Hengyuan Zhang, David Paz, Yuliang Guo +3
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sens…
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Hengyuan Zhang, David Paz, Yuliang Guo +5
Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when ge…
SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations
Narayanan Elavathur Ranganatha, Hengyuan Zhang, Shashank Venkatramani +2
Vector maps are essential in autonomous driving for tasks like localization and planning, yet their creation and maintenance are notably costly. While recent advances in online vec…
OSM vs HD Maps: Map Representations for Trajectory Prediction
Jing-Yan Liao, Parth Doshi, Zihan Zhang +2
While High Definition (HD) Maps have long been favored for their precise depictions of static road elements, their accessibility constraints and susceptibility to rapid environment…
Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation
David Paz, Narayanan E. Ranganatha, Srinidhi K. Srinivas +2
This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios. To facilitate th…